{"id":"W4386824661","doi":"10.1021/acs.analchem.3c02477","title":"Quantitative Comparison of Capture-SELEX, GO-SELEX, and Gold-SELEX for Enrichment of Aptamers","year":2023,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Aptamer; Systematic evolution of ligands by exponential enrichment; Chemistry; DNA; SELEX Aptamer Technique; Computational biology; Molecular biology; Biology; Biochemistry; RNA; Gene","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001565221,0.0001778349,0.0004228829,0.00004142196,0.00003254533,0.000007394574,0.0001333319,0.000204062,0.000003209964],"category_scores_gemma":[0.0002397854,0.0001579101,0.0001836846,0.0002613543,0.000306197,0.000003115479,0.00008725733,0.00009115653,8.463067e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001284022,"about_ca_system_score_gemma":0.0000378666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001098057,"about_ca_topic_score_gemma":0.000004878027,"domain_scores_codex":[0.9988027,0.00001802259,0.0004000034,0.0003746805,0.0001659747,0.0002386434],"domain_scores_gemma":[0.99918,0.0000775387,0.0001948306,0.0002677123,0.0001907682,0.00008918668],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001561925,0.0001014601,0.001987258,0.0001923179,0.0002243743,0.000001015848,0.00003778737,0.00001562427,0.9912949,0.0002025226,0.004419968,0.001366543],"study_design_scores_gemma":[0.0003026464,0.0002358503,0.0002938237,0.00003000008,0.000143582,0.000001953017,0.000559669,0.003298579,0.9903676,0.0001760749,0.004397031,0.0001932005],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951845,0.0003254158,0.002592293,0.00018351,0.00001677547,0.0001559673,0.0001064043,0.00003868202,0.001396389],"genre_scores_gemma":[0.9933459,0.0002057731,0.004824642,0.00003574179,0.00004277478,0.000008884364,0.0002628216,0.00001638539,0.001257096],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003282954,"threshold_uncertainty_score":0.6439388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02624038765871082,"score_gpt":0.3501363489285115,"score_spread":0.3238959612698006,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}